Surrogate Safety Measures (SSMs) are used to express road safety in terms of the safety risk in traffic conflicts. Typically, SSMs rely on assumptions regarding the future evolution of traffic participant trajectories to generate a measure of risk, restricting their applicability to scenarios where these assumptions are valid. In response to this limitation, we present the novel Probabilistic RISk Measure derivAtion (PRISMA) method. The objective of the PRISMA method is to derive SSMs that can be used to calculate in real time the probability of a specific event (e.g., a crash). The PRISMA method adopts a data-driven approach to predict the possible future traffic participant trajectories, thereby reducing the reliance on specific assumptions regarding these trajectories. Since the PRISMA is not bound to specific assumptions, the PRISMA method offers the ability to derive multiple SSMs for various scenarios. The occurrence probability of the specified event is based on simulations and combined with a regression model, this enables our derived SSMs to make real-time risk estimations. To illustrate the PRISMA method, an SSM is derived for risk evaluation during longitudinal traffic interactions. Since there is no known method to objectively estimate risk from first principles, i.e., there is no known risk ground truth, it is very difficult, if not impossible, to objectively compare the relative merits of two SSMs. Instead, we provide a method for benchmarking our derived SSM with respect to expected risk trends. The application of the benchmarking illustrates that the SSM matches the expected risk trends. Whereas the derived SSM shows the potential of the PRISMA method, future work involves applying the approach for other types of traffic conflicts, such as lateral traffic conflicts or interactions with vulnerable road users.

Many studies about conducting activities while traveling start from the hypothesis that conducting onboard activities reduces the value of time (VoT). However, surprisingly limited empirical evidence is provided for this hypothesis. The few studies that aim at providing this evidence face methodological problems in the sense that effects attributed to conducting onboard activities are confounded with differences between groups. This paper further develops and applies a solution for this problem proposed by Wardman and Lyons (2016). In essence, this method includes constructing a within-person choice experiment, which involves that the same respondents make choices in a context that enables conducting activities, as well as in a context that does not enable conducting activities. This method is applied in a study that collected data from 820 train travelers in the Netherlands. The results show that as expected, the VoT in the activity context is significantly lower than the VoT in the non-activity context, which thus supports the hypothesis. Reduction in VoT due to conducting onboard activities is around 30% for commuters, while leisure travelers who prefer to read lose almost half their VoT value. In addition, this paper discusses how the estimated VoT reduction values can be interpreted as the Value of Activity (VoA), which can be used for appraising investments aimed at reducing the disutility of travel other than by means of reducing travel time, such as improving Internet connections.